5. Building the framework for a simulation.
Before we start to populate our simulation with agents, we should understand how the world works. Economists have produced innumerable theories about the laws that govern societies – often idealizing and simplifying the world to be able to express them in simple equations. I would like to see another approach, using machine learning to deduce how the economy functions.
Take a country with good statistics and describe every county or municipality (except a few that are used to check the models' predictions). Begin with basic parameters like demographics (number of inhabitants, age, born and dead last year, age of death, moving in and out, occupations, distribution of income, unemployment) and production (agriculture, natural resources, industry).
From there it's easy to find direct correlations between parameters (we don't need to worry about causation). There are also methods to find complex correlations and patterns.
When you do this, you can write a rough equation of state for a society, that best fits the data, using different coefficients for each variable (an equation of state for a gas uses pressure, volume, number of moles and temperature, as variables). So if you know everything mentioned above for a municipality, except for one thing, for example median income, you can calculate that.
After that we can fine-tune the equation by putting in less obvious characteristics like education, communications, house prices, climate, religious beliefs and so on.
If we then continue to do this, not only for the present, but for as far back as we have statistics, we can start to see interesting trends. We can put in the percentage of the population that had access to internet or owned a smart phone, and see how these inventions have affected societies. Maybe we can detect how new laws changed things. This will be especially noticeable when we continue to do the same for more countries, with different laws and economic systems.
Now we have an equation of state that will interact with the agents we put in the model. The agents' actions changes the variables in the equation, but the outcome of the equation also influences the agents' decisions.
Related Wikipedia articles:
It would be interesting to see if we could find an equation of state for the mind. Then you would have all the different personality traits on one side of the equation and the result when you put in measured values for them, would always be zero. It's not obvious that they relate to each other in a way, that this is possible.
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